AI Monitoring, Governance KPIs, and Incident Response Pack

Purpose: Operate AI governance after deployment by connecting the control catalogue to measurable metrics, periodic evidence, and an action tracker that nobody has to chase down by email.

The AI Control Monitoring & Governance KPI Workbook helps provide:

• Control catalogue with owner, monitoring frequency, test method, and linked KPI.

• KPI and KRI catalogue with direction, unit, target, amber limit, and automatic status (green / amber / red / not measured).

• Monitoring log that calculates status and trend from each recorded measurement.

• Control test record with design effectiveness, operating effectiveness, and evidence quality.

• Action tracker that turns red KPIs or failed controls into assigned tasks with a due date and status.

• Management dashboard showing active KPIs by status (green / amber / red / not measured), failed and partially effective controls, overdue actions, and overdue KPI or control test reviews.

Example Use Case: A vendor updates the underlying model behind an AI system without formal notice, changing its outputs in subtle ways. Without a control catalogue linked to a coverage KPI, nobody notices the drift until a complaint shows up three months later. The Workbook is designed to expose that gap at the very first period measurement.

Simple Artifacts to Produce: (1) a control catalogue linked to measurable KPIs and KRIs; (2) a monitoring log with automatic status and trend calculation; and (3) an action tracker connecting red flags to assigned owners and due dates.

Purpose: Serve as the authoritative operational record for every AI incident, from the first suspicion through formal closure and post-incident review.

The AI Incident Management Workbook helps provide:

• Incident register with recommended severity, kill-switch criteria, and escalation route calculated automatically.

• Triage and decisions: response role, decision authority, and communications and notification assessment.

• Timeline and evidence to reconstruct events, decisions, and communications in chronological order.

• Actions and recovery, including exit criteria, validation evidence, and the reactivation approver.

• PIR and lessons learned, connected back to the control catalogue and KPIs.

• Dashboard showing open incidents, kill-switch use, average containment time, and trends by type.

Example Use Case: A recommendation model starts producing biased results for a subgroup of users. The team hesitates, unsure whether this qualifies as an AI incident, and delays opening a record while seeking confirmation. The Workbook is designed so that the uncertainty itself triggers case creation. Severity and classification can be refined later, not before.

Simple Artifacts to Produce: (1) a complete incident register with automated severity and escalation calculation; (2) a chronological timeline of events, decisions, and evidence; and (3) a PIR record linking lessons learned back to controls and KPIs.

Purpose: Guide the full incident lifecycle, detect, triage, contain, investigate, eradicate, recover, communicate, and close, with practical steps the response team can follow in real time.

The AI Incident Response Playbook helps provide:

• Quick response checklist and incident command brief (Appendix A).

• Severity matrix that maps impact, scope, and control state to recommended severity (Appendix B).

• Communication templates for internal alerts, executive updates, and user notices (Appendix C).

• Tracker integration guidance connecting Playbook activities to the Incident Management Workbook (Appendix D).

• Scenario playbooks for harmful output, bias, privacy leakage, prompt injection, model drift, unauthorized agentic action, and vendor failures.

• Closure checklist that confirms root cause, corrective actions, residual risk, and lessons learned before closing the case.

Example Use Case: An AI agent with tool access executes an action outside its approved scope. The technical team contains the symptom but does not document the decision or the scope of the restriction. The Playbook is designed so that every step, from containment to communication, gets logged as reconstructible evidence.

Simple Artifacts to Produce: (1) a quick-response checklist and incident command brief; (2) a set of scenario-specific playbooks covering common AI incident types; and (3) a closure checklist confirming root cause, corrective actions, and lessons learned.

Purpose: Define in advance who decides, who executes, and what level of restriction to apply when an AI system needs to be paused, isolated, rolled back, or reactivated.

The AI Escalation & Kill-Switch Guidance helps provide:

• Pre-deployment readiness checklist for high-impact or agentic systems.

• Escalation triggers and routes by category: people and safety, rights, privacy, information security, autonomy and control, and more.

• Pause/kill-switch statuses, from "traffic restricted" to "full pause" and "reactivated."

• Decision matrix, authority, and RACI to prevent hesitation during critical moments.

• Step-by-step execution procedure, including "break-glass" authority for when delay would increase harm.

• Reactivation criteria checklist, plus a readiness test script and decision-record templates (Appendices A through D).

Example Use Case: A model endpoint is compromised, and the team debates whether stopping the full service will affect critical operations. Without guidance in place beforehand, the decision gets made under pressure and without clear authority. The Guidance is designed so that decision (scope, approver, evidence) is already defined before the event happens.

Simple Artifacts to Produce: (1) a pre-deployment readiness checklist for high-impact systems; (2) a decision matrix with defined authority and RACI; and (3) a reactivation criteria checklist with decision-record templates.